With the volume of vehicles using roadways today, traffic detection and management has become ever important. Advanced traffic control technologies have employed machine vision to improve the vehicle detection and information extraction at a traffic scene over previous point detection technologies, such as loop detectors. Machine vision systems typically consist of a video camera overlooking a section of the roadway and a processor that processes the images received from the video camera. The processor then detects the presence of a vehicle and extracts other traffic related information from the video image.
An example of such a machine vision system is described in U.S. Pat. No. 4,847,772 to Michalopoulos et al., and further described in Panos G. Michalopoulos, Vechicle Detection Video Through Image Processing: The Autoscope System, IEEE Transactions on Vehicular Technology, Vol. 40, No. 1, February 1991. The Michalopoulos et al. patent discloses a video detection system including a video camera for providing a video image of the traffic scene, means for selecting a portion of the image for processing, and processor means for processing the selected portion of the image.
Before a machine vision system can perform any traffic management capabilities, the system must be able to detect vehicles within the video images. An example of a machine vision system that can detect vehicles within the images is described in commonly-assigned U.S. patent application Ser. No. 08/163,820 to Brady et al., filed Dec. 8, 1993, entitled "Method and Apparatus for Machine Vision Classification and Tracking." The Brady et al. system detects and classifies vehicles in real-time from images provided by video cameras overlooking a roadway scene. After images are acquired in real-time by the video cameras, the processor performs edge element detection, determining the magnitude of vertical and horizontal edge element intensities for each pixel of the image. Then, a vector with magnitude and angle is computed for each pixel from the horizontal and vertical edge element intensity data. Fuzzy set theory is applied to the vectors in a region of interest to fuzzify the angle and location data, as weighted by the magnitude of the intensities. Data from applying the fuzzy set theory is used to create a single vector characterizing the entire region of interest. Finally, a neural network analyzes the single vector and classifies the vehicle.
When machine vision systems analyze images, it is preferable to determine what areas of the image contains the interesting information at a particular time. By differentiating between areas within the entire image, a portion of the image can be analyzed to determine the importance of the information therein. One way to find the interesting information is to divide the acquired image into regions and specific regions of interest may be selected which meet predetermined criteria. In the traffic management context, another way to predetermine what areas of the image will usually contain interesting information is to note where the roadway is in the image and where the lane boundaries are within the roadway. Then, areas off the roadway will usually contain less information relevant to traffic management, except in extraordinary circumstances, such as vehicles going off the road, at which time the areas off the roadway will contain the most relevant information. One way to delineate the roadway in machine vision systems is to manually place road markers on the edges of the roadway. Then, a computer operator can enter the location of the markers on the computer screen and store the locations to memory. This method, however, requires considerable manual labor, and is particularly undesirable when there are large numbers of installations.
Another problem that machine vision systems face arises when attempting to align consecutive regions of interest. Typically, translation variant representations of regions of interest, or images, are acquired by the machine vision system. Therefore, alignment of these translation variant representations can be difficult, particularly when the detected or tracked object is not traveling in a straight line. When the edges of the roadway and the lane boundaries are delineated, however, it facilitates alignment of consecutive regions of interest because when the tracked object is framed, it becomes more translationally invariant. In the traffic management context, regions can be centered over the center of each lane to facilitate framing the vehicle within the regions, thereby making the representations of the regions of interest more translationally invariant.